Helmet wearing determination method, helmet wearing determination system, helmet wearing determination apparatus, and program

ABSTRACT

The present invention is directed to a helmet wearing determination system including a imaging means that is installed in a predetermined position and images a two-wheel vehicle that travels on a road; and a helmet wearing determination means that processes an image imaged by the imaging means, estimates a rider head region corresponding to a head of a person who rides on the two-wheel vehicle that travels on the road, compares image characteristics of the rider head region with image characteristics according to the head at a time when a helmet is worn or/and at a time when a helmet is not worn, and determines whether or not the rider wears the helmet.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a divisional application of U.S. patentapplication Ser. No. 15/037,875 filed on May 19, 2016, which is aNational Stage Entry of international application PCT/JP2014/079825,filed Nov. 11, 2014, which claims the benefit of priority from JapanesePatent Application 2013-239598 filed on Nov. 20, 2013, the disclosuresof all of which are incorporated in their entirety by reference herein.

TECHNICAL FIELD

The present invention relates to a helmet wearing determination method,a helmet wearing determination system, a helmet wearing determinationapparatus, and a program.

BACKGROUND ART

Recently, from a standpoint of security, a technology for monitoring amobile object such as an automobile that travels on a road is proposed.Further, the technology is a technology for determining and detectingwhether a mobile object that travels on a road is any one of anautomobile (four-wheel vehicle), a bike (two-wheel vehicle), a bicycle(two-wheel vehicle), and a pedestrian.

In particular, from a standpoint of safety and illegality, whether aperson who rides on the two-wheel vehicle wears a helmet is expected tobe detected.

To cope with the above, a technology for detecting whether the personwho rides on the two-wheel vehicle wears the helmet is proposed (forexample, see Patent Literature 1).

In the invention of Patent Literature 1, an image processing unitprocesses an image produced from a camera and detects an imaged person.When the person is detected, the image processing unit determineswhether the person wears the helmet. In a determination method, a faceand a head of a person are first detected and on the basis of a ratiobetween a width of the face and a width of the head, it is determinedwhether the helmet is worn.

CITATION LIST Patent Literature

Patent Literature 1: Japanese Laid-open Patent Publication No.2010-211427

SUMMARY OF INVENTION Technical Problem

However, in the invention of Patent Literature 1, image processing fordetecting a face and a head of a person is required. When a type of ahelmet, for example, a full-face type or jet type helmet is worn, a faceis hidden in the helmet and it is difficult to detect the face.

Further, suppose that on the basis of a ratio between a width of a faceand a width of a head, it is determined whether or not the helmet isworn. In the case where the helmet is not worn and in the case where afull-face type helmet is worn, both of the ratios between the widths ofthe faces and the widths of the heads of the person are approximatelyequal to one, and therefore it is difficult to determine whether or notthe helmet is worn.

The present invention is made to solve the above problem. Therefore, thepurpose of the present invention is to provide a helmet wearingdetermination method, a helmet wearing determination system, a helmetwearing determination apparatus, and a program capable of detecting withhigh accuracy whether or not a person who rides on a two-wheel vehiclewears the helmet.

Solution to Problem

The present invention is directed to a helmet wearing determinationsystem including a imaging means that is installed in a predeterminedposition and images a two-wheel vehicle that travels on a road; and ahelmet wearing determination means that processes an image imaged by theimaging means, estimates a rider head region corresponding to a head ofa person who rides on the two-wheel vehicle that travels on the road,compares image characteristics of the rider head region with imagecharacteristics according to the head at a time when a helmet is wornor/and at a time when a helmet is not worn, and determines whether ornot the rider wears the helmet.

The present invention is directed to a helmet wearing determinationmethod including the steps of processing an image of an imaging devicethat images a road, estimating a rider head region corresponding to ahead of a person who rides on a two-wheel vehicle that travels on theroad, comparing image characteristics extracted from the rider headregion with image characteristics according to the head at a time when ahelmet is worn or/and at a time when a helmet is not worn, anddetermining whether or not the rider wears the helmet.

The present invention is directed to a helmet wearing determinationapparatus including a helmet wearing determination means that processesan image of an imaging device that images a road, estimates a rider headregion corresponding to a head of a person who rides on a two-wheelvehicle that travels on the road, compares image characteristics of therider head region with image characteristics according to the head at atime when a helmet is worn or/and at a time when a helmet is not worn,and determines whether or not the rider wears the helmet.

The present invention is directed to a program for causing a computer toexecute a process of processing an image of an imaging device thatimages a road and estimating a rider head region corresponding to a headof a person who rides on a two-wheel vehicle that travels on the road;and a process of comparing image characteristics of the rider headregion with image characteristics according to the head at a time when ahelmet is worn or/and at a time when a helmet is not worn anddetermining whether or not the rider wears the helmet.

Advantageous Effects of Invention

According to the present invention, on the basis of image of thetwo-wheel vehicle that travels on the road, it can be detected with highaccuracy whether or not the person who rides on the two-wheel vehiclewears the helmet.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating a concept of the presentinvention.

FIG. 2 illustrates the present invention.

FIG. 3 is a block diagram illustrating a two-wheel vehicle riding personnumber determination system according to an embodiment of the presentinvention.

FIG. 4 illustrates an installation location of an imaging device 1.

FIG. 5 illustrates the embodiment of the present invention.

FIG. 6 illustrates the embodiment of the present invention.

FIG. 7 illustrates the embodiment of the present invention.

FIG. 8 illustrates the embodiment of the present invention.

DESCRIPTION OF EMBODIMENTS

An embodiment of the present invention will be described with referenceto the accompanying drawings.

First, a concept of the present invention will be described. FIG. 1 is ablock diagram illustrating the concept of the present invention. In FIG.1 , a reference numeral 1 denotes an imaging device, and a referencenumeral 2 denotes a helmet wearing determination apparatus.

The imaging device 1 is installed in a predetermined position on alateral side of a road on which a two-wheel vehicle travels, and imagesthe two-wheel vehicle that travels on the road. In addition, thetwo-wheel vehicle travels is, for example, motor cycle such as bikes, abicycle, etc.

The helmet wearing determination apparatus 2 processes an image of theimaging device 1 and estimates a rider head region corresponding to ahead of a person who rides on the two-wheel vehicle that travels on theroad. Further, the helmet wearing determination apparatus 2 comparesimage characteristics of the rider head region with imagecharacteristics according to the head at the time when a helmet is wornor/and at the time when a helmet is not worn, and determines whether ornot a rider wears the helmet.

In particular, the present invention is characterized in that the imagecharacteristics of the head (the entire shape including the head and aface) at the time when a person wears a helmet and the imagecharacteristics of the head (the entire shape including the head and theface) at the time when a person does not wear a helmet each have uniqueimage characteristics, and by using the above, the present inventiondetermines whether or not the person who rides on the two-wheel vehiclewears the helmet.

Like a conventional technology, by using the above-describedcharacteristics, the present invention can determine whether or not aperson who rides on the two-wheel vehicle wears the helmet, withoutnecessity to recognize a face of the person who rides on the two-wheelvehicle.

Next, operations of the helmet wearing determination apparatus 2 will bedescribed with reference to FIG. 2 .

As illustrated in FIG. 2 , an image in a region in the upper position ofthe two-wheel vehicle is first extracted from the image imaged by theimaging device 1. As an extraction method, the entire image in which adriver rides on the two-wheel vehicle is extracted, and then the imagein the region in the range of a predetermined upper position is takenout from the entire image. Alternatively, from the entire image in whichthe driver rides on the two-wheel vehicle, a vehicle body unit of thetwo-wheel vehicle may be specified, and the image in the region in thepredetermined range of the upper vehicle body unit may be extracted.

Next, the rider head region corresponding to the head of the person whorides on the two-wheel vehicle is estimated from the extracted image inthe upper region. The head of the person is a spherical body, and evenif the helmet is worn, a contour of the head is circular to some extent,and further, the contour has not a shape in which an edge of the hump ispointed. When the shapes of the heads estimated to be the heads of thepersons are previously learnt and a database of the head shapes is made,the rider head region corresponding to the head of the rider can bespecified from the extracted region.

Further, the shapes according to the heads of the persons can bespecified from shapes of valley portions of the image contour in theupper position. In the case where two persons ride on the two-wheelvehicle, for example, a peculiar valley portion formed by a line from ahead and a back of a person who rides on a front seat of the two-wheelvehicle up to arms, a chest, and a head of a person who rides on a rearseat of the two-wheel vehicle is generated between the heads.Accordingly, when the peculiar shape of the valley portion is previouslylearnt and the database is made, the rider head region including thehead can be specified from the contour shape in the upper position.Further, two methods as described above are combined, and therebyspecific accuracy of the rider head region including the head can beimproved more.

As described above, the rider head region corresponding to the head ofthe person who rides on the two-wheel vehicle is estimated.

Next, the image characteristics of the rider head region are comparedwith the image characteristics according to the head at the time whenthe helmet is worn or the image characteristics according to the head atthe time when the helmet is not worn. The image of the head at the timewhen the helmet is worn has peculiar image characteristics. In afull-face type, jet type, or cap type helmet, for example, an image froma direction in which a face is imaged has peculiar characteristics atthe time when their helmets are worn. On the other hand, also the imageof the head at the time when the helmet is not worn has peculiar imagecharacteristics. To cope with the above, the peculiar imagecharacteristics of the image of the head at the time when the helmet isworn and the peculiar image characteristics of the image of the head atthe time when the helmet is not worn are previously learnt and thedatabase is made. By using the above database, the image characteristicsof the rider head region are discriminated, and it is determined whetheror not the rider wears the helmet.

Next, a specific embodiment of the present invention will be described.

FIG. 3 is a block diagram illustrating a helmet wearing determinationsystem according to the embodiment of the present invention.

The helmet wearing determination system according to the embodiment ofthe present invention includes the imaging device 1, the helmet wearingdetermination apparatus 2, and a reporting device 3.

The imaging device 1 images the two-wheel vehicle that travels on aroad, and is installed in a position in which an image in which theabove-described rider head region is easily detected can be imaged.

Specifically, as illustrated in FIG. 4 , in the range from a front faceup to a lateral side (mainly, an obliquely front side) with respect to atraveling direction of the two-wheel vehicle, the imaging device 1 isinstalled in a position in which the two-wheel vehicle can be imaged.The helmet includes several types of full-face type, jet type, and captype. Therefore, when the two-wheel vehicle is imaged from a directionin which a face of the person who rides on the two-wheel vehicle isimaged, a difference easily shows up between the case where the helmetis worn and the case where the helmet is not worn. Further, the imagingdevice 1 is preferably installed short of a spot in which the two-wheelvehicle decelerates, such as a traffic intersection, a temporary stopline, a curve, and a speed bump. The two-wheel vehicle is imaged shortof the spot in which the two-wheel vehicle decelerates, and thereby animage in which a motion blur is small is easily acquired. As a result,characteristics can be easily extracted from the image and accuracy inthe determination whether or not the helmet is worn can be improved.

The helmet wearing determination apparatus 2 includes a mobile objectdetection unit 21, a category classification unit 22, a vehicle bodyposition detection unit 23, a rider head region estimation unit 24, ahelmet wearing determination unit 25, a category determinationdictionary 26, a vehicle body detection dictionary 27, a rider headregion estimation dictionary 28, and a helmet wearing determinationdictionary 29.

The mobile object detection unit 21 detects a mobile object that movesin an image from the imaging device 1. In a method for detecting themobile object, various types of methods are conventionally proposed andan appropriate method may be selected.

The category classification unit 22 specifies the two-wheel vehicle(bike) from among the mobile objects by using the category determinationdictionary 26, and supplies to the vehicle body position detection unit23 the entire image of the two-wheel vehicle (bike) that moves. Adatabase of information for specifying the two-wheel vehicle (bike) ismade and registered in the category determination dictionary 26.

Specifically, the category classification unit 22 receives detection ofthe mobile objects from the mobile object detection unit 21, andclassifies categories of the mobile objects. In the classification ofthe category of the mobile object, a size of a search range fordiscriminating the category of the mobile objects is previouslydetermined and a probability that the mobile object is any mobile objectother than the two-wheel vehicle is calculated on the basis of acharacteristic amount in the search range. On the basis of the aboveresults, any mobile object other than the two-wheel vehicle is excluded.In the predetermined search range, for example, characteristics that twocircles are aligned approximately linearly can be used ascharacteristics for calculating the probability that the mobile objectis any mobile object other than the two-wheel vehicle. Further,characteristics of positions of headlights or the number thereof can beused as characteristics for calculating the probability that the mobileobject is any mobile object other than the two-wheel vehicle (bike). Acharacteristic portion of the image as described above is previouslylearnt and a database of the above data is made as the categorydetermination dictionary 25. Further, by using the categorydetermination dictionary 25, the category classification unit 22determines a probability that the detected mobile object is any mobileobject other than the two-wheel vehicle. In the case where thisprobability is more than a predetermined threshold, the categoryclassification unit 22 prevents the entire image of the mobile objectfrom being supplied to the vehicle body position detection unit 23. Onthe other hand, in the case where the probability is not more than thepredetermined threshold, the category classification unit 22 suppliesthe entire image of the detected mobile object to the vehicle bodyposition detection unit 23. By using the category determinationdictionary 25, for example, the category classification unit 22 preventsthe entire image of the detected mobile object that is determined to beany mobile object other than the two-wheel vehicle with the probabilityof 90% (=a predetermined threshold) from being supplied to the vehiclebody position detection unit 23. On the other hand, since there is alsothe possibility that the mobile object that is determined to be anymobile object other than the two-wheel vehicle with the possibility of90% or less, for example, 85% is the two-wheel vehicle, the categoryclassification unit 22 supplies the entire image of the mobile object tothe vehicle body position detection unit 23 to perform a detailedverification.

On the basis of the entire image of the mobile object produced from thecategory classification unit 22, the vehicle body position detectionunit 23 specifies a range according to the vehicle body unit of thetwo-wheel vehicle by using the vehicle body detection dictionary 26. Inthe specification of this range, for example, in an example (an imageimaged from the lateral side) of the entire image of the two-wheelvehicle (bike) illustrated in FIG. 5 , for example, a range in which alength between a front end on the lateral side of a front wheel and arear end on the lateral side of a rear wheel of the two-wheel vehicle isset as a horizontal width and a length in a longitudinal direction ofthe wheel is set as a vertical width is specified as a range of thevehicle body unit. Further, in an example (an image imaged from theobliquely front side) of the entire image of the two-wheel vehicle(bike) illustrated in FIG. 6 , a range in which a length between a frontend on the lateral side of the front wheel and a rear end on the lateralside of the rear wheel of the two-wheel vehicle is set as a horizontalwidth, and that is slightly larger than a width between a front end onthe upper side and a front end on the installation side of the frontwheel and the rear wheel of the two-wheel vehicle is specified as arange of the vehicle body unit. A wheel shape and a distance betweenwheels of the two-wheel vehicle that is required to be specified arepreviously learnt and a database of the above data is made as thevehicle body detection dictionary 27.

In addition, only images of the two-wheel vehicle are not necessarilyproduced from the category classification unit 22. Therefore, thevehicle body position detection unit 23 cannot specify the vehicle bodyunit of the two-wheel vehicle by using the vehicle body detectiondictionary 26 in some cases. In this case, on the premise that themobile object is not the two-wheel vehicle, the mobile object isexcluded from objects of processing.

From the upper region of the vehicle body specified by the vehicle bodyposition detection unit 23, the rider head region estimation unit 24detects the rider head region estimated to be the head of the rider. Asa detection method, the contour shape according to the head of theperson is detected from the contour shape of the image in the upperregion of the vehicle body specified by the vehicle body positiondetection unit 23, and the predetermined-size region surrounding thecontour shape is detected as the rider head region. The head of theperson is a spherical body and even if the helmet is worn, a contourshape of the head is circular to some extent. By using the abovecharacteristics, humped shapes having the above-describedcharacteristics of the head of the person are previously learnt andregistered in the rider head region estimation dictionary 28. In thecase where two persons or more ride on the two-wheel vehicle, a peculiarvalley portion formed by a line from a head and a back of a person thatrides on a front seat of the two-wheel vehicle up to arms, a chest, anda head of a person that rides on a rear seat of the two-wheel vehicle isgenerated between the heads. Accordingly, when the peculiar shape of thevalley portions is previously learnt and registered in the rider headregion estimation dictionary 28, the rider head region including thehead can be specified from the contour shape in the upper position.Further, two methods as described above are combined, and therebyspecific accuracy of the rider head region including the head can beimproved more.

Further, the helmet wearing determination unit 25 compares the imagecharacteristics of the rider head region with the image characteristicsaccording to the head at the time when the helmet is worn or the imagecharacteristics according to the head at the time when the helmet is notworn, and determines whether or not the rider wears the helmet.

For example, the image of the head of the rider who wears a full-facetype, jet type, or cap type helmet has peculiar characteristics asillustrated in FIG. 7 . On the other hand, also the image of the head ofthe rider at the time when the helmet is not worn has peculiarcharacteristics as illustrated in FIG. 7 . As the peculiarcharacteristics as described above, the image of the head has, forexample, a characteristic that a straight line is generated in a formcrossing a face in a relatively upper portion of the central head at thetime when the helmet is worn. Further, as another characteristic, theimage of the head has a characteristic that the head shape at the timewhen the helmet is worn is made rounded with bright clarity, as comparedto the head shape at the time when the helmet is not worn. Further, asanother characteristic, the image of the head has a characteristic thatthe head shape at the time when the helmet is worn is fixed through atime-series, and on the other hand, the head shape at the time when thehelmet is not worn is not fixed when hair swings in the wind. The imagecharacteristics of the above-described characteristics are previouslylearnt by using HOG characteristics, luminance gradient characteristics(direction characteristics), CCS characteristics, and Haar likecharacteristics, and by using Support Vector Machine, GeneralizedLearning Vector Quantization, AdaBoost, Real AdaBoost, and pseudo Bayesidentification as a statistical identification method. Further, theimage characteristics are registered in the helmet wearing determinationdictionary 29.

Further, by using the helmet wearing determination dictionary 29, thehelmet wearing determination unit 25 determines whether the imagecharacteristics of the rider head region have a resemblance to either ofthe image characteristics at the time when the helmet is worn and theimage characteristics at the time when the helmet is not worn. Further,the helmet wearing determination unit 25 determines on the basis of theabove whether or not the person who rides on the two-wheel vehicle ofthe image wears the helmet.

By using the helmet wearing determination dictionary 29 in which theabove-described characteristics are stored, for example, on the basis ofthe image characteristics of the head of the rider head region, in thecase where it can be detected that a straight line is generated in aform crossing a face in a relatively upper portion of the central head,it is determined that the helmet is worn. On the other hand, in the casewhere it cannot be detected that a straight line is generated in a formcrossing a face in a relatively upper portion of the central head, it isdetermined that the helmet is not worn.

By using the helmet wearing determination dictionary 29 in which theabove-described characteristics are stored, on the basis of the imagecharacteristics of the head of the rider head region, in the case whereit can be detected that the head shape is made rounded with brightclarity, it is determined that the helmet is worn. On the other hand, inthe case where it cannot be detected that the head shape is made roundedwith bright clarity, it is determined that the helmet is not worn.

Further, by using the helmet wearing determination dictionary 29 inwhich the above-described characteristics are stored, on the basis ofthe image characteristics of the head of the rider head region, in thecase where it can be detected that a change in a time-series is fixed,it is determined that the helmet is worn. On the other hand, in the casewhere it cannot be detected that a change in time-series is fixed, it isdetermined that the helmet is not worn.

The reporting device 3 has a display unit 30 that displays the image ofthe imaging device 1, and reports determination results of the helmetwearing determination unit 25 to a monitor.

The reporting device 3 receives the determination results of the helmetwearing determination unit 25, coordinates of the region surrounding therider and the two-wheel vehicle body from the vehicle body positiondetection unit 23, and coordinates of the rider head region from therider head region estimation unit 24. Further, as illustrated in FIG. 8, the image of the imaging device 1 is displayed on the display unit 30,and at the same time, in the case where the determination results of thehelmet wearing determination unit 25 are that the rider does not wearthe helmet, a graphic enclosing a region including at least thetwo-wheel vehicle body and the rider head is displayed on the image anda graphic enclosing the rider head region is displayed thereon.

Further, the characteristics of the rider (for example, a driver) may bedisplayed to be easily understood (for example, enlarged). Examples ofthe characteristics of the rider (for example, the driver) include aface, clothes, a bicycle (a color, a type of bicycles, conversion,etc.), and the like. By using a technology of facial recognition orobject recognition, the above characteristics are recognized from theimage, the recognized portion is segmented from the image, and thesegmented image is largely displayed separately from the image asillustrated in FIG. 8 (for example, a face of the driver is largelydisplayed). Through the above display, the characteristics of the rider(for example, the driver) can be grasped.

Further, in combination with a person collation system or an objectcollation system, the above-described recognized characteristics (forexample, a face, clothes, a color of bicycles, a type of bicycles,conversion, etc.) may be collated with information about the personcollation system or information about the object collation system, andcollation results may be displayed. Examples of the collation resultsinclude information about criminal records, a maker of clothes, a typeof bicycles/conversion contents, and the like. Through the aboveconfiguration, the reporting device 3 can be also used for prevention orexposure of criminals.

According to the present embodiment configured as described above, itcan be determined whether or not the person who rides on the two-wheelvehicle wears the helmet, without necessity to recognize a face of theperson who rides on the two-wheel vehicle.

Further, in the case where the person who rides on the two-wheel vehicledoes not wear the helmet, the two-wheel vehicle and the head on whichthe helmet is not worn are displayed so as to be recognized, andtherefore awareness of the monitor can be promoted.

Further, in the two-wheel vehicle riding person number determinationapparatus 2, the helmet wearing determination unit 25 may include aninfrared detection unit (not illustrated). The infrared detection unitmay be, for example, an infrared sensor. On the basis of detectionresults of infrared rays from the infrared detection unit, for example,the helmet wearing determination unit 25 may determine whether or notthe helmet is worn. Alternatively, the helmet wearing determination unit25 may impose a weight on comparison results of the above-describedimage characteristics on the basis of the detection results of infraredrays, and determine whether or not the helmet is worn. By including theinfrared detection unit, the helmet wearing determination unit 25 candetermine with higher accuracy whether or not the helmet is worn.

In the embodiment as described above, the two-wheel vehicle ridingperson number determination apparatus 2 is configured by hardware, andcan be configured also by programs for making an information processingdevice execute the above-described operations. In this case, a processorthat is operated by programs stored in a program memory implementsfunctions and/or operations similar to those in the embodimentsdescribed above. Moreover, only part of functions of the embodimentsdescribed above may be implemented by a computer program.

Further, in the embodiment as described above, a determination object isdescribed as the two-wheel vehicle. Further, when the head of the personcan be detected, the determination object is not limited to thetwo-wheel vehicle. Of course, the present invention is applicable alsoto a one-wheel vehicle, a three-wheel vehicle, and others.

Moreover, part or all of the preceding embodiments may be described asin the following Supplementary Notes, although not limited thereto.

[Supplementary Note 1] A helmet wearing determination system comprising:

an imaging means that is installed in a predetermined position andimages a two-wheel vehicle that travels on a road; and

a helmet wearing determination means that processes an image imaged bythe imaging means, estimates a rider head region corresponding to a headof a person who rides on the two-wheel vehicle that travels on the road,compares image characteristics of the rider head region with imagecharacteristics according to the head at a time when a helmet is wornor/and at a time when a helmet is not worn, and determines whether ornot the rider wears the helmet.

[Supplementary Note 2] The helmet wearing determination system accordingto Supplementary Note 1, wherein

the helmet wearing determination means compares the imagecharacteristics of the rider head region with the image characteristicsthat are previously learnt and stored, according to the head at the timewhen the helmet is worn or/and at the time when the helmet is not worn,and determines whether or not the rider wears the helmet.

[Supplementary Note 3] The helmet wearing determination system accordingto Supplementary Note 1 or 2, wherein

the rider head region estimation means specifies a position of thetwo-wheel vehicle from the image, and estimates the rider head regionfrom the image region in an upper position of the specified two-wheelvehicle.

[Supplementary Note 4] The helmet wearing determination system accordingto any one of Supplementary Notes 1 to 3, further comprising a reportingmeans that reports determination results of the helmet wearingdetermination means.

[Supplementary Note 5] The helmet wearing determination system accordingto Supplementary Note 4, wherein

when the determination results of the helmet wearing determination meansare that the helmet is not worn, the reporting means reports thedetermination results.

[Supplementary Note 6] The helmet wearing determination system accordingto Supplementary Note 5, wherein

the reporting means has a display means that displays the image, and

displays a graphic enclosing a region including at least a two-wheelvehicle body and the rider head specified by the rider head regionestimation means on the image when the determination results of thehelmet wearing determination means are that the helmet is not worn.

[Supplementary Note 7] The helmet wearing determination system accordingto any one of Supplementary Notes 1 to 6, wherein

the imaging means is installed in a position in which the two-wheelvehicle can be imaged in a range from an obliquely front side up to alateral side with respect to a traveling direction of the two-wheelvehicle.

[Supplementary Note 8] The helmet wearing determination system accordingto any one of Supplementary Notes 1 to 7, wherein

the imaging means is installed short of a spot in which the two-wheelvehicle decelerates.

[Supplementary Note 9] A helmet wearing determination method, comprisingthe steps of:

processing an image of a imaging device that images a road, estimating arider head region corresponding to a head of a person who rides on atwo-wheel vehicle that travels on the road, comparing imagecharacteristics extracted from the rider head region with imagecharacteristics according to the head at a time when a helmet is wornor/and at a time when a helmet is not worn, and determining whether ornot the rider wears the helmet.

[Supplementary Note 10] The helmet wearing determination methodaccording to Supplementary Note 9, wherein

the image characteristics according to the head at the time when thehelmet is worn or/and at the time when the helmet is not worn arepreviously learnt.

[Supplementary Note 11] The helmet wearing determination methodaccording to Supplementary Note 9 or 10, wherein

a position of the two-wheel vehicle from the image is specified, and

the rider head region is estimated from the image region in an upperposition of the specified two-wheel vehicle.

[Supplementary Note 12] The helmet wearing determination methodaccording to any one of Supplementary Notes 9 to 11, further comprisingthe step of reporting the determination results.

[Supplementary Note 13] The helmet wearing determination methodaccording to Supplementary Note 12, wherein

when the determination results are that the helmet is not worn, thedetermination results are reported.

[Supplementary Note 14] The helmet wearing determination methodaccording to Supplementary Note 13, wherein

when the determination results are that the helmet is not worn, agraphic enclosing a region including at least the two-wheel vehicle bodyand the rider head is displayed on the image and the determinationresults are reported.

[Supplementary Note 15] The helmet wearing determination methodaccording to any one of Supplementary Notes 9 to 14, wherein

the two-wheel vehicle is imaged in a range from an obliquely front sideup to a lateral side with respect to a traveling direction of thetwo-wheel vehicle.

[Supplementary Note 16] The helmet wearing determination methodaccording to Supplementary Note 15, wherein

the two-wheel vehicle is imaged short of a spot in which the two-wheelvehicle decelerates.

[Supplementary Note 17] A helmet wearing determination apparatus,comprising:

a helmet wearing determination means that processes an image of aimaging device that images a road, estimates a rider head regioncorresponding to a head of a person who rides on a two-wheel vehiclethat travels on the road, compares image characteristics of the riderhead region with image characteristics according to the head at a timewhen a helmet is worn or/and at a time when a helmet is not worn, anddetermines whether or not the rider wears the helmet.

[Supplementary Note 18] A program for causing a computer to execute: aprocess of processing an image of an imaging device that images a roadand estimating a rider head region corresponding to a head of a personwho rides on a two-wheel vehicle that travels on the road; and

a process of comparing image characteristics of the rider head regionwith image characteristics according to the head at a time when a helmetis worn or/and at a time when a helmet is not worn and determiningwhether or not the rider wears the helmet.

Each embodiment as described above is a mere preferable embodiment ofthe present invention and thus the present invention will not be limitedonly to the embodiment. It is possible to carry out the presentinvention with various changes and modifications without departing fromthe spirit and scope of the invention.

The present application claims priority based on Japanese PatentApplication No. 2013-239598 filed on Nov. 20, 2013, disclosure of whichis incorporated herein in its entirety.

REFERENCE SIGNS LIST

-   -   1 Imaging device    -   2 Helmet wearing determination apparatus    -   3 Reporting device    -   21 Mobile object detection unit    -   22 Category classification unit    -   23 Vehicle body position detection unit    -   24 Rider head region estimation unit    -   25 Helmet wearing determination unit    -   26 Category determination dictionary    -   27 Vehicle body detection dictionary    -   28 Rider head region estimation dictionary    -   29 Helmet wearing determination dictionary    -   30 Display unit

The invention claimed is:
 1. A helmet wearing determination system,comprising: at least one memory storing instructions; and at least oneprocessor executing the instructions to perform: learning images in acase that a helmet is worn and images in a case that the helmet is notworn; registering, in a helmet wearing determination dictionary, resultsof learning the images in the case that the helmet is worn and theimages in the case that the helmet is not worn; determining, accordingto the results of learning the images in the case that the helmet isworn and the images in the case that the helmet is not worn, whether thehelmet is worn or the helmet is not worn based on an image captured byan imaging capturing unit, wherein the registering comprises registeringthe learning results of the learning as a learning model.
 2. The helmetwearing determination system according to claim 1, wherein the at leastone processor performs: learning image characteristics of a head regionin the case that the helmet is worn and image characteristics of a headregion in the case that the helmet is not worn.
 3. The helmet wearingdetermination system according to claim 1, wherein the at least oneprocessor performs: determining, according to the helmet wearingdetermination dictionary, whether the helmet is worn or the helmet isnot worn by comparing the image captured by the imaging capturing unitwith the images in the case that the helmet is worn and the images inthe case that the helmet is not worn.
 4. The helmet wearingdetermination system according to claim 1, wherein the at least oneprocessor performs: determining, according to the helmet wearingdetermination dictionary, whether the helmet is worn or the helmet isnot worn by determining image captured by the imaging capturing unitwith the images in the case that the helmet is worn and the images inthe case that the helmet is not worn.
 5. The helmet wearingdetermination system according to claim 1, wherein the at least oneprocessor performs: controlling a display to display results ofdetermining whether the helmet is worn or the helmet is not worn.
 6. Thehelmet wearing determination system according to claim 5, wherein the atleast one processor performs: controlling the display to display a headnot wearing the helmet so as to be recognized in a case that the resultsof determining whether the helmet is worn or the helmet is not wornindicate that the helmet is not worn.
 7. The helmet wearingdetermination system according to claim 5, wherein the at least oneprocessor performs: controlling the display to display the image,captured by the image capturing unit, and to display a figurecorresponding the results of determining whether the helmet is worn orthe helmet is not worn, the figure being superimposed on the image. 8.The helmet wearing determination system according to claim 7, whereinthe figure is a figure which surrounds an area including a head.
 9. Thehelmet wearing determination system according to claim 5, wherein the atleast one processor performs: controlling the display to display theimage captured, by the image capturing unit, and a figure whichsurrounds an area including a head being superimposed on the image. 10.The helmet wearing determination system according to claim 1, whereinlearning the images in the case that the helmet is worn and the imagesin the case that the helmet is not worn comprises learning imagecharacteristics in the case that the helmet is worn and imagecharacteristics in the case that the helmet is not worn, and determiningwhether the helmet is worn or the helmet is not worn comprisesdetermining, according to the results of learning the images in the casethat the helmet is worn and the images in the case that the helmet isnot worn, whether the helmet is worn or the helmet is not worn based onthe image characteristics of the image captured by the imaging capturingunit.
 11. A helmet wearing determination method, comprising the stepsof: learning images in a case that a helmet is worn and images in a casethat the helmet is not worn; registering, in a helmet wearingdetermination dictionary, results of the learning the images in the casethat the helmet is worn and the images in the case that the helmet isnot worn; determining, according to the results of learning the imagesin the case that the helmet is worn and the images in the case that thehelmet is not worn, whether the helmet is worn or the helmet is not wornbased on an image captured by an imaging capturing unit, wherein theregistering comprises registering the learning results of the learningas a learning model.
 12. The helmet wearing determination methodaccording to claim 11, wherein learning the images in the case that thehelmet is worn and the images in the case that the helmet is not worncomprises learning image characteristics in the case that the helmet isworn and image characteristics in the case that the helmet is not worn,and determining whether the helmet is worn or the helmet is not worncomprises determining, according to the results of learning the imagesin the case that the helmet is worn and the images in the case that thehelmet is not worn, whether the helmet is worn or the helmet is not wornbased on the image characteristics of the image captured by the imagingcapturing unit.
 13. The helmet wearing determination method according toclaim 11, comprising the step of: learning image characteristics of ahead region in the case that the helmet is worn and imagecharacteristics of a head region in the case that the helmet is notworn.
 14. The helmet wearing determination method according to claim 11,further comprising: controlling a display to display results ofdetermining whether the helmet is worn or the helmet is not worn.
 15. Anon-transitory recording medium storing a program for causing a computerto execute: learning images in a case that a helmet is worn and imagesin a case that the helmet is not worn; registering, in a helmet wearingdetermination dictionary, results of the learning the images in the casethat the helmet is worn and the images in the case that the helmet isnot worn; determining, according to the results of learning the imagesin the case that the helmet is worn and the images in the case that thehelmet is not worn, whether the helmet is worn or the helmet is not wornbased on an image captured by an imaging capturing unit, wherein theregistering comprises registering the learning results of the learningas a learning model.
 16. The non-transitory recording medium accordingto claim 15, wherein learning is learning image characteristics in thecase that the helmet is worn and image characteristics in the case thatthe helmet is not worn, and wherein the determining is determining,according to the results of the learning, whether the helmet is worn orthe helmet is not worn based on image characteristics of the imagecaptured by the imaging capturing unit.
 17. The non-transitory recordingmedium according to claim 15, wherein the program causes the computer toexecute: learning image characteristics of a head region in the casethat the helmet is worn and image characteristics of a head region inthe case that the helmet is not worn.